Mining Simulation with Optimization скачать в хорошем качестве

Mining Simulation with Optimization

https://mosimtec.com/mining-simulatio... An example mining simulation model demonstrating an embedded optimization model for blending stockpiles. A blend optimizer is required to identify which stockpiles should be used to create a consistent composite feed for the processing plant or furnace. --- This simulation model represents a mine where ore is generated from surface or underground operations and enters a series of equipment until metal is recovered and transported. This type of model runs on user-defined inputs where output data; including statistics on throughput, inventory, utilization, and recovery; can be easily exported to the user in graphical and numerical form. Using the data generated from the model, the decision-maker can understand how their mine is expected to perform. A model of this type can answer questions such as “How will maintenance affect the system?”, “How will the attributes of ore coming out of the mines; such as grade, carbon-content and sulfides; affect equipment utilization, throughput, and inventory?”, “Can a change in equipment and storage configuration improve the system?”, and “Can other mathematical tools, such as optimization, be used to improve a process and increase upfront metal recovery?” Ore enters the system in surface and underground operations where each slug of ore is assigned attributes, such as grade and carbon-content, based on a user-defined mine plan. Ore goes through a series of equipment before it is transported to processing operations where it enters one of many stockpiles, waiting for pyrometallurgical processing. For each blending cycle, ore is taken out of the stockpiles to feed processing operations. How the ore is blended across the stockpiles will affect equipment performance and metal recovery. In this demo model, the user has the ability to turn on a blending optimizer. When the optimizer is turned on, the stockpile attributes; including weight, grade, and carbon-content; are analyzed to create the optimal blend of ore. Integrating this optimizing algorithm into a simulation model is extremely useful because this blending technique can be easily tested in the virtual mine over varying periods of time with a variety of constraints and scenarios. Its effects on equipment performance, throughput, and metal recovery can be evaluated before the blending optimizer is implemented in a real mine. With a simulation model, business decisions can be made based on the output data by testing different scenarios and input parameters. Simulation models are also necessary to test optimization algorithms and other tools so their effects on the system can be anticipated before they are implemented in a real mine. MOSIMTEC 297 Herndon Pkwy #302 Herndon, VA 20170 (855) 677-3342